K2 Horizon opens more than model weights — including training data, code and intermediate checkpoints

The Institute of Foundation Models (IFM) released K2 Horizon on September 3 as a family of six models ranging from a 0.9B dense model to a 375B mixture-of-experts flagship. IFM says the release opens the training lifecycle rather than only final weights: architecture details, model code, training configurations, data or detailed data-construction recipes, evaluation resources, logs and intermediate checkpoints are part of the published/release plan. The model and code license is Apache 2.0, while datasets retain their own applicable licenses. Hugging Face already hosts the model collection and public K2 training-data resources, but individual model cards still say some intermediate checkpoints, training data or training code 'will be released', so the completeness of the open package varies by model at launch.

Open-weight releases let builders run a model; reproducible releases let researchers inspect how the model got there. K2 Horizon gives developers and researchers substantially more material for auditing training choices, studying capability changes across checkpoints and building derivative models without reverse-engineering an opaque recipe. The smaller 0.9B, 3.7B and 7B variants also make that research possible below data-center scale. The practical caveat is that IFM’s performance claims are self-reported and some promised artifacts are still propagating across the public repositories, so builders should verify the exact files available for the specific K2 model they plan to use.

K2 Horizon includes 0.9B, 3.7B, 7B and 32B dense models plus a 36B model activating roughly 4B parameters and a 375B flagship activating roughly 23B. IFM positions the smaller models for constrained and on-device use and the sparse models for larger local or enterprise inference.

IFM says it is publishing training data or detailed construction recipes, code, configurations, mixture information, fine-grained logs, evaluation resources and intermediate checkpoints. This makes it possible to study how capabilities change during training instead of evaluating only a final weight file.

The Hugging Face collection is live and includes final model checkpoints plus K2 training-data resources. At the same time, several individual model cards say intermediate checkpoints and some training assets will be released. Builders should therefore treat 'fully open' as the intended release architecture and inspect the current repository state before assuming every promised artifact is already downloadable.

IFM reports strong reasoning, coding, agentic and long-context performance across the family, including state-of-the-art claims at smaller sizes. Those comparisons are project-authored. The unusually open training package makes independent replication easier, but it does not replace it.